Rohlings Interpretive Method: How Can a Flexible Battery Perform Like a Fixed Battery

1
Rohlings Interpretive Method How Can a Flexible
Battery Perform Like a Fixed Battery

• Martin L. Rohling, Ph.D.
• Associate Professor
• Department of Psychology
• University of South Alabama

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Clinical vs. Mechanical Diagnosis

• Much research has been conducted since Meehl
  (1954) found clinical judgment to be less
  accurate than mechanical or actuarial judgement
• e.g., Dawes, Faust, Meehl (1989) Filskov
  (1981) Garb (1989) Garb (1994) Garb (1998)
  Grove et al. (2000) Sawyer (1966) and Wedding
  Faust (1989)
• Such results influential in causing NPs to turn
  to different versions of the HRB (Russell, 1998).
• Batteries have been defined as the method by
  which one can avoid the clinical errors
  highlighted by Meehl an others, using actuarial
  rules for diagnosis (Russell, 1995 Russell et
  al., 2005).

3
Rohlings Interpretive Method (RIM) Development
History

• Conducted several meta-analysis with Dr. Laurence
  Binder at the Portland, OR VA
• The last of these focused on the residual
  cognitive effects of mild head injury.
• Binder, Rohling, Larrabee (1997)
• Binder et al. grouped effect sizes (ES) into
  domains of neuropsychological functioning based
  on factor analytic studies.
• e.g., Leonberger, Nicks, Larrabee, Goldfader
  (1992)

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RIM Generated fromMeta-Analytic Procedures

• Meta-analysis (MA) combines effect sizes (ES)
  across samples assuming that they all sample the
  population M for the particular effect of
  interest.
• Common method ES calculation is a standardized
  mean difference score (e.g., Glass delta).
• delta difference between con. exp. groups
  Ms divided by con. groups SD.
• delta analogous to Z score - linear equivalent of
  T score used in clinical neuropsychology

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RIM Generated fromMeta-Analytic Procedures

• Binder et al. (1997) combined ESs generated from
  various tests into cognitive domains.
• Why not similarly combine ESs, or T scores, from
  a single patient into cognitive domains in the
  same way that it is accomplished in MA.
• Each test score is treated as a ES that reflects
  the individuals ability within a domain.
• ES can be combined based on homogeneity of
  variance, so as to avoid combining apples and
  oranges.

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Introduction to the RIM Analysis

• Flexible battery (multiple measure) use
• Is the most frequently cited model of assessment
  among neuropsychologists.
• Only 7 of neuropsychologists use a fixed battery
  (Rabin et al, 2006, ACN).
• Regarding the suitability, practicality, and
  usefulness of any fixed battery
• We know of no batteries that fully satisfy these
  criteria.
• (Lezak, Howieson Loring 2004, Neuropsych.
  Assess., 4th ed, p 648.)

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Advantages of Flexible Battery

• Dynamic responsive to clinicians needs
• Covers 1 or many domains
• Flexible, can be adapted for each patient
• Can oversample domains
• Well suited for hypothesis-driven approach

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Potential Problems with aFlexible Battery

• Inflated error rates
• Multicollinearity
• Weighting decision problems
• Unknown veracity/reliability of sets of tasks
• Human judgment errors

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Human Judgment Errors(Wedding Faust, 1989, ACN)

• Hindsight bias
• Confirmatory bias
• Overreliance on salient data
• Under-utilization of base rates
• Failure to take into account co-variation

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Potential Benefits withRohlings Interpretive
Method (RIM)

• Judgment errors can threaten reliability
  validity of multiple measure test batteries.
• RIM was designed to reduce these effects.
• Based on meta-analytic techniques.
• Uses a linear combination of scores placed on a
  common metric.

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Potential Benefits of RIM

• A strategy that produces summary results
  analogous to those generated in a fixed-battery
  approach (e.g., HII, GNDS, AIR).
• Takes advantage of psychometric properties of
  same metric data, e.g., T Scores.

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Todays Presentation - Intent

• Present a set of procedures that allows for a
  quantitatively-based comparison of an overall
  battery of measures.
• Non-specific to battery measures themselves.
• Can be used for any individual patient.
• Demonstrate importance and practicality of use
  of established statistical indices.
• (e.g., alpha, beta, effect size).

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Todays Intent (contd)

• Present a data format for any set of measures to
  be inspected at
• Global level (OTBM)
• Domain level (DTBM)
• Test measure level (ITBM)
• Present a series of calculations to assist in the
  generation of these indices.
• Present Steps in conjunction with clinical
  judgment from an informed position.

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Common RIM Domains of Functioning

• Symptom Validity (SV) Tests
• Emotional / Personality (EP) Measures
• Meta-Cognition, Pain, or other self-ratings
• Estimated Premorbid General Ability (EPGA)
• Test Battery Means
• Overall (OTBM), Domain (DTBM), Instrument
  (ITBM)
• Cognitive Domains
• VC, PO, EF, AML, VML, AW, PS
• Non-Cognitive Domains
• PM, LA, SP

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Sample RIM Summary Table
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Sample RIM Graphic Display
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Brief of RIM Steps

• There are 24 steps to the RIM process
• 17 calculation steps
• Advice on design of the battery
• Calculation of summary statistics
• Generation of graphic displays
• 7 interpretative steps.
• Detail a systematic procedure for use of the
  statistical summary table and graphic displays
  to
• Assess and verify summary data.
• Identify strengths/limitations of current data.
• Obtain a reliable diagnosis.
• Develop tx plans based on sound judgments.
• We briefly review each step in just a moment.

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Support for the RIM Process

• Rational support/reasoning Reduce clinical
  judgment errors.
• The RIM is a Process, not a program.
• Rather, the RIM is a way of formalizing thinking
  interpretation of individual case data.
• This is operationalizing what many flexible
  battery clinicians are already doing in their
  head.

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Support for the RIM ProcessSpecific Advantages

• Psychometric properties at level with fixed,
  co-normed batteries, without their limitations.
• Flexibility of test selection.
• Flexibility of theoretical view of cognition
  (domain structure)

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Support for the RIM ProcessSpecific Advantages

• Quantitatively support your conclusions and
  interpretations
• Statistical evaluation
• Measure of confidence in findings
• Measure of limitations of findings
• Ability to present data at different levels of
  interpretation
• Greater defensibility

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The RIM has a Set of Procedure or Specific Steps
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RIM Steps 1-4 Summary Data

• Design administer battery.
• Use well standardized recently normed tests.
• Estimate premorbid general ability.
• Use Reading (WTAR), Regression (OPIE-III),
  academic records (rank, SAT, ACT).
• Convert test scores to a common metric.
• We recommend T scores, but z or SS OK too.
• Assign scores to domains.
• Factor analysis to support assignment (Tulsky et
  al., 2003)

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RIM Steps 5-8 Summary Data

• Calculate domain M, sd, n.
• Calculate test battery means (TBM).
• Overall TBM All scores, large N high power.
• Domain TBM Avoids domain over weighting.
• (e.g., attention memory).
• Instrument TBM One score per norm sample.
• Calculate p for heterogeneity.
• Have you put apples oranges together?
• Determine categories of impairment.
• Recommend using of Heaton et al. (2003).

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RIM Steps 9-12 Summary Data

• Determine of test impaired.
• Analogous to Halstead Impairment Index
• scores below cutoff / total of of scores
• Calculate ES for all summary stats.
• Use Cohens d (Me Mc) / SD pooled
• Calculate confidence interval for stats.
• 90 CI 1.645 x SEM
• Upper limit of performance for impair.
• Look for overlap between 90 CI of EPGA (lower)
  Summary Stats (upper)

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RIM Steps 13-17 Summary Data

• Conduct one-sample t tests.
• Use EPGA as reference point
• Conduct a between-subjects ANOVA.
• Looking for strengths weaknesses
• Conduct power analyses.
• Only needed for those NS differences
• Sort scores for visual inspection.
• Graphically display summary statistics.

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RIM Steps 18-20 Interpretation

• Assess battery validity.
• Examine the Symptom Validity scores.
• Caution in accepting low power results.
• Look at heterogeneity of summary stats.
• Normative sample unrepresentative of patient.
• Scores assigned to wrong domain.
• Inconsistent performance on construct measures.
• Examine influence of psychopathology.
• Examine scores for heterogeneity.
• Check OTBM, DTBM, ITBM impaired.

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RIM Steps 21-24 Interpretation

• Examine strengths/weaknesses looking at
• Confidence intervals overlap.
• Results from one-sample t tests.
• Results of ANOVA.
• TI show differences otherwise not evident.
• Determine if pattern existed premorbidly.
• Examine non-cognitive domains.
• Psychomotor, Lang/Aphasia, Sensory Percept
• Explore Type II errors need more tests?
• Examine sorted T-scores
• Look for patterns missed by summary stats.

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RIM Sample Case 1 Obvious TBI

• Age 37
• Handed Left
• Race Euro-American
• Sex Female
• Ed 14 years
• Occup Nursing
• Marital Sep. 10 yrs
• Living Camper in parents backyard

• Reason for Referral
• TBI in head-on boat accident. Propeller hit pt in
  right parietal-occipital lobe (LOC 7 days GCS
  3). Eval. to determine capacity for medical
  financial decisions, parenting skills,
  occupational prognosis, disability status.
  Significant emotional, behavioral, occupational,
  and social problems pre-TBI.

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RIM Sample Case 1 Obvious TBI
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RIM Sample Case 1 Obvious TBI
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TBI Dose Response CurvesDikmen ESs Meyers T
Scores
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Combined Dikmen Meyers Estimates ES, T,
Difference
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Return to Work Study OTBMs for 4 Groups of TBI
Survivors
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RIM Sample Case 1 Obvious TBI Normal
Distribution of T Scores
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RIM Sample Case 2 Subtle Diabetes

• Reason for Referral
• 2 yrs dangerous work habits. Eval to see if
  atrial fib Type II diabetes impairs cognition.
  Hospitalized TIA-like Sx. Admitted to problems
  for 20 yrs, cardiac dysrhythmia bradycardia,
  pacemaker, blood sugar difficult to manage,
  family Hx of heart disease diabetes.

• Age 55
• Handed Right
• Race Euro-American
• Sex Male
• Ed 13 years
• Occup Mechanic
• Marital Married 20 yr
• Living at home w/wife

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RIM Sample Case 2 Subtle Diabetes
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RIM Sample Case 2 Subtle Diabetes
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RIM Sample Case 2 Subtle Diabetes Normal
Distribution of T Scores
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RIM Critiques Concern 1

• The method of calculating the standard deviations
  (SDs) for summary statistics and domain scores is
  incorrect.
• Since many of the remaining steps of the RIM
  depend on the use of these SDs, this error is
  magnified in the subsequent steps.
• SDs statistically can not exceed 9.99 and are
  more likely to be around 6.4

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Response 1 RIM Ms 4 Datasets
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Inter-Individual Ms SDs
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Response 1 RIM SDs 4 Datasets
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Intra-Individual Ms SDs
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RIM Critiques Concern 2

• More false-positives then clinical judgment.
• Palmer et al. (2004) expressed concern that
• We failed to distinguish statistical from
  clinical significance.
• This failure is a critical error that precludes
  the prudent clinician from using the RIM.

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Response 2 RIM vs. Manual Detecting Differences
Overall
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Response 2 RIM vs. Manual Detecting
Differences ESs
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Response 2 RIM vs. ManualDetecting Differences
Scores
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RIM Critiques Concern 3

• Clinicians who use the RIM will
• Idiosyncratically assign scores to cognitive
  domains.
• This will result in low inter-rater reliability
  in analysis diagnosis.

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RIM Critiques Concern 4

• Scores on domains are unit weighted, which
  introduces error.
• Willson Reynolds (2004) said scores load on
  multiple domains. Assignment to domains weights
  depend on
• Battery of tests administered.
• Patients whose test scores are being examined.

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Response 4 Cross-Valid. Unit Wts

• Conducted 4 multiple reg. on 457 pts WAIS-R.
• Split sample in ½ - assess shrinkage.
• Regressed patients verbal subtests onto PIQ.
• Generated ideal weights for the 1st ½ of sample.
• Used wts to predict PIQs in the 2nd ½ of sample.
• Pre-PIQs regressed on actual PIQs 2nd ½ sample.
• Also, generated weights for the 2nd ½ of sample.
• Use Pre-PIQs regress on actual PIQs 1st ½
  sample.
• Repeated, except performance subtests predict VIQ
• split sample ½ generate same statistics as
  before.

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Response 4 Cross-Valid. Unit Wts

• Purpose of these procedures
• How much variance in wts. is sample specific.
• Amount of shrinkage using cross-validated wts.
• Shrinkage error compared to error introduced by
  using unit wts vs. ideal wts.
• Results 98 of the variance accounted for with
  unit wts. Compared to ideal weights.
• Support use of unit wts. Rather than ideal wts.
• See, Dawes, R. M. (1979).

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RIM Critiques Concern 5

• Multiple measures used to generate composite
  scores
• Results in less accurate estimates of the
  cognitive domains.

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Response 5 Estimate FSIQ Using Scaled Score
Meanss
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RIM Critiques Concern 6

• A general ability factor is used to represent
  premorbid functioning for all domains.
• This not supported by the literature.
• This results in inaccurate conclusions regarding
  degree of impairment suffered by a patient in
  each cognitive domains assessed.

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Domain Means CorrelationsAll were Significant (
p lt .001 )
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RIM Critiques Concern 7

• Norms used come from samples that are of
  undocumented comparability.
• Furthermore, even when norms used were generated
  from different but comparable samples, their
  format prohibits ready comparisons.

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Response 7 Split-Half Reliability

• Analyze Dataset 2 OTBMs from 42 DVs
• Individuals data split into two sets
• 21 test variables for each OTBM (1 2)
• 2 independent OTBMs created for pt.
• Split DVs intentionally-separated so no
  normative sample included both OTBMs

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Response 7 Split-Half Reliability

• Results r .81, 66 of variance accounted
• Slope of the regression line was .82 (SE .027)
• Intercept 9.2 (SE 1.20).
• Mean OTBM-1 45.0 (sd 7.3)
• Mean OTBM-2 43.6 (sd 7.2)
• Results simulate worse case scenario.
• used an entirely different set of norms.
• Est. test-retest r for OTBM 42 DVs increased
  r from .81 to .88 using the Spearman-Brown
  correction).

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Response 7 Split-Half Reliability

• No overlap in normative samples.
• Worst-case condition, generally administer
  instruments (e.g., WAIS-III) with OTBMs generated
  from co-normed variables.
• Meyers Rohling test-retest reliability of .86.
• When different norms used, often gave same
  instruments (e.g., AVLT or RCFT)
• No instrument used OTBM-1 included OTBM-2
• Heaton et al.s (2001) - schizophrenic pts.
• Obtained a test-retest reliability of .97.
• Comparing 2 identical batteries, not worst-case.

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RIM Critiques Concern 8

• The RIM will result in an undue inflation of
  clinicians confidence.
• Such overconfidence results in more error in a
  interpretation, not less.

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RIM vs. Tulsky et al. (2003) Case 1
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RIM vs. Tulsky et al. (2003) Case 2
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Summary of the Rohling Interpretive Method of
Statistical Analysis of Individual
Neuropsychological Test Data
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Summary of RIM Steps

• 24 total steps to the process
• 17 calculation steps
• Battery Design
• Calculation of summary statistics
• Generation of graphic displays
• 7 interpretative steps
• Use of summary table and graphic displays to
• Assess and verify summary data
• Identify strengths/limitations of current data
• Obtain a reliable diagnosis
• Develop tx plans based on clinical judgments.

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Summary of RIM Advantages

• Formulize thinking interpretation of data
• Operationalize what you already do.
• Reduce judgment errors thru RIM Process.
• Take advantage of psychometric properties at
  level with fixed, co-normed batteries.
• Allows flexibility of test selection.
• Allows flexibility of theoretical view of
  cognition (e.g., domain structure)

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Summary of RIM Advantages contd

• Gives Quantitative support for your conclusions
  and interpretations
• Statistical evaluation
• Measure of confidence in findings
• Measure of limitations of findings
• Ability to present data at different levels of
  interpretation
• Equals greater defensibility

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Our RIM Cautions/Concerns

• Does not replace clinical judgment, rather,
  informs clinical judgment.
• This still means CJ errors are possible.
• Susceptibility T-Scores to distrib. deviance
• Process, not program
• Pre-morbid ability estimates
• Domain selection, test placement

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RIM is Not Alone Out There!

• Dawn Flanagan, Ph.D., at St. Johns University in
  New York independently developed a similar method
• The Cattell-Horn-Carroll (CHC) Cross Battery
  Approach.
• Second edition of Essentials of Cross-Battery
  Assessment (Flanagan, Ortiz, Alfonso, in press)
  is due out in March, which explains her method,
  along with co-authors
• Some of her work can also be found on the website
  by Dumont-Willis.

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Published Research Findings Using the RIM

• 1) RIM vs. HRB
• 2) Variance Accounted for by SVT
• 3) Effect of Depression on NP Results
• 4) Prediction of Employment after TBI

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RIM of HRB OTBM vs. HII

• Heaton et al.s (1991) HRB norms for OTBM
• T Score (M50, sd10)
• OTBM r with HII -.79
• (p lt .0001)
• 62 variance account.
• Over predicts low
• Under predicts high

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RIM of HRB OTBM vs. GNDS

• OTBM r with GNDS -.87
• 76 variance acc.
• OTBM neither under nor over predicts across range
  of GNDS
• Intercept impairment is T Score 46.0
• Reitan Wolfson (1993)
• (GNDS 29)

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RIM of HRB OTBMs Relationship to Global Indices
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RIM of HRB Diagnostic Classification Using the
HII
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RIM of HRB Cross-Validation of RIM using HRB in
2 Samples

• Regressed Dikmen Meyers TBI data
• Generated a predicted HII for pts in OK dataset.
• Correlation actual predicted HII .95
• Sen .60, Spec .77, PPV .78, NPV .59
• Overall Correct Classification 71
• Predicted HII from MNBs OTBM got a more
  accurate indicator of impairment than actual HII

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Factor Loadings of Domain Scores
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M SDs of Composite Z scores
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Mean Z score on Objective Tests

• Small diff. between Gen. Normal Gen. Neuro. on
  NPT
• No diff. between Exag. Normal Exag. Neuro on
  NPT
• Deficits for Exag. Neuro were more modest than
  for Exag. Normals on SVT
• Interaction between Validity Neuro Status.

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Mean Z score Self-Report

• No diff. between Gen. Neuro. Exag. Neuro on
  Memory Complaints
• No diff. between Gen. Exag. Neuro on Psych.
  symptoms
• Deficits for Exag. Normal on Psych. symptoms
  Memory Complaints, the latter is larger
• Interaction between Validity Neuro Status.

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Depression Study Reference

• Rohling, M. L., Green, P., Allen, L. M.,
  Iverson, G. L. (2002). Depressive symptoms and
  neurocognitive test scores in patients passing
  symptom validity tests. Archives of Clinical
  Neuropsychology, 17, 205-222.

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Mood Group Assignment

• Patients classified into 2 subgroups
• From entire sample, 420 passed all SVTs
• Sample split based on BDI
• Low-Depressed 25ile on BDI (lt 10)
• n 178, M 6 (3)
• High-Depressed 75ile on BDI (gt 25)
• n 187, M 31 (6)

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Depression Study Participants

• All 365 patients referred for evaluation for
  compensation-related purposes
• All diagnostic groups included
• 53 Head injury referrals
• 22 Medical referrals
• 14 Psychiatric referrals
• 11 Other neurological
• Age 42 (11) Ed 13 (3) Sex 64 males
  Non-English 18 Handedness 9 Left

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Results Mood Validity Status
SVT Status
Mood BDI
Genuine
Exaggerating
175 (48)
Depress 75ile
186 (52)
NonDep 25ile
266 (74)
95 (26)
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Results Sample Split by Validity
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Effect of Mood Depends on Effort

• Exaggerating patients accounted for
• 39 of High-Dep group
• 14 of Low-Dep group
• Mood Effort used as IVs and Cognition DV
• Effect for effort, no effect for mood
• However, when Memory Complaints DV
• Effects for both effort and mood
• Also, when other Emotion/Personality DV
• Effects for both effort and mood

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Effect of Mood Depends on Effort

• When both mood groups were included in
  regression analysis, as predicted
• Memory ratings related to mood
• (r .60 p lt .0001)
• Mood not correlated with cognition
• (r .10 p gt .10)
• Memory ratings not related to cognition
• (r .13, p .06)

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Mood Replication

• Gervais pain sample findings (n 177)
• Exaggerating patients accounted for
• 55 of High-Dep 33 of Low-Dep group
• Memory ratings related to mood (r .55)
• Mood not correlated with cognition (r .06)
• Memory ratings related to cognition (r .15)
• Group means correlated with Greens .94
• all patient (High-D, Low-D, Gen, Exag).

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Effect if Pain on OTBM
88
Effect if Pain on OTBM
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Return to Work after Injury

• Three main hypotheses using MNB-RIM
• OTBM will predict return to work level
• Cognitive domain that will be most predictive
  will be executive function
• Adding the Patient Competency Rating Scale will
  improve work prediction
• PCRS is by Prigatano (1985)

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Return to Work ANOVA of OTBM
91
Logistic Regression Using OTBM
92
Return to Work Summary

• OTBM differences between groups
• Disabled /Unemployed not able to separate
• Below/At Previous not able to separate
• Collapsed groups result in 71 correct
• above base rate of 52 correct

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Return to Work Domain Analysis

• Executive function not the most predictive
• Most of variance carried by Perceptual
  Organization Working Memory
• Using Cognitive Domains
• OTBM increases Correct from 71 to 74
• Incremental validity of PCRS very low.
• 7 of the variance

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Return to Work Domain Analysis

• By including premorbid variables, increases
  diagnostic accuracy most helpful being
• Premorbid IQ, level of occupation, education
• Including acute measures increases accuracy most
  helpful being
• LOC group
• Time since injury

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Depression Study Conclusions

• Memory complaints not synonymous with impairment
  in compensation sample
• Findings replicated
• Effort accounts for more variance in self-ratings
  of cognition objective performance than mood
• Findings replicated

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Whats wrong with this patient-1? (Key RCPS)

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Whats wrong with this patient-1? (Key RCPS)
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Whats wrong with this patient-2? (Key JSVD)
99
Whats wrong with this patient-2? (Key JSVD)
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Whats wrong with this patient-3? (Key NPAD)
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Whats wrong with this patient-3? (Key NPAD)
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Whats wrong with this patient-4? (Key SMAA)
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Whats wrong with this patient-4? (Key SMAA)
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Rohlings Interpretive Method Use of
Meta-Analytic Procedures for Single Case Data
Analysis

• Martin L. Rohling
• Questions Comments Welcome!

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CT/MRI Data

• Participant Demographic Information
• Variable Sample Sizes (N 124)
• Gender
• Male 82
• Female 42
• Ethnicity
• Caucasian 119
• Other 5

106
CT/MRI

• Diagnostic Groups Sample Size
• MVA/TBI 47
• Blow to Head 32
• LCVA 24
• RCVA 21

107
CT/MRI

• All were Right Handed.
• All were followed by Dr. Meyers through
  hospitalization and rehabilitation.
• None were involved in litigation.
• All passed internal validity checks.

108
CT/MRI

• CT/MRI Location
• Left Frontal 59
• Left Parietal 37
• Left Temporal 34
• Left Occipital 6
• Right Frontal 40
• Right Parietal 42
• Right Temporal 31
• Right Occipital 3

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CT/MRI

• All were given MNB
• CT/MRI data coded for injury reported on MRI/CT
  at the time of injury
• Present 1
• Absent 0

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CT/MRI

• Independent Sample 1-tailed t-test on each lobe
• On CT/MRI report
• Present 1
• Absent 0

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CT/MRI Data
114
Brain Regions Involved in the Performance of
WAIS-III Arithmetic
115
Brain Regions Involved in the Performance of the
Boston Naming Test
116
Brain Regions Involved in the Performance of the
Rey-CFT Copy
117
Brain Regions Involved in the Performance of the
AVLT Total Score
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CT/MRI

• NP tests generally behaved as expected
• A more Systemic or Domain like approach
  better at explaining results
• Construct of Executive Function not supported.

119
Domains used by the MNB

• Attention/Working Memory
• Digit Span
• Forced Choice
• Animal Naming
• Sentence Rep
• AVLT 1

• Processing Speed/Mental Flexibility
• Digit Symbol
• Dichotic Both
• Trails A
• Trails B

120
Domains used by the MNB

• Verbal Reasoning
• Similarities
• Arithmetic
• Information
• COWA
• Dichotic Left
• Dichotic Right
• Boston Naming
• Token Test

• Visual Reasoning
• Picture Completion
• Block Design
• JOL
• Category
• RCFT Copy

121
Domains used by the MNB

• Verbal Memory
• AVLT Total
• AVLT Immediate
• AVLT Delayed
• AVLT Recognition

• Visual Memory
• RCFT Immediate
• RCFT Delayed
• RCFT Recognition

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Domains used by the MNB

• Motor and Sensory
• Finger Tapping Dominant Hand
• Finger Tapping Non-Dominant Hand
• Finger Localization Dominant Hand
• Finger Localization Non-Dominant Hand

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Commonality of Reduced O2
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Domain Consistency

• N 936
• Passed all validity checks
• No missing data
• Not involved in litigation
• Calculated Domain Ms
• Regression used to predict Domain Ms using all
  on other Domain Ms

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Domain Means Correlations

• 1 2 3 4 5 6
• 1 Premorbid .76 .71 .62 .56 .79
• 2 - OTBM .76 .98 .81 .82 .84
• 3 - DTBM .71 .98 .77 .79 .78
• 4 - Attent/Work Mem .64 .81 .77 .64 .69
• 5 Pro Spd/Mental Flex .62 .82 .79 .64 .72
• 6 - Verbal Reason .79 .84 .78 .69 .72
• 7 - Visual Reason .68 .81 .81 .54 .64 .64
• 8 - Verbal Memory .53 .77 .78 .68 .50 .54
• 9 - Visual Memory .54 .77 .80 .53 .55 .55
• 10 - Dom Motor/Sensory .30 .54 .62 .37 .44 .36
• 11 - Nond Motor/Sensory .28 .53 .62 .31 .44 .30
• All were Significant p lt .001

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Domain Ms Correlations (cont.)

• 7 8 9 10 11
• 1 - Premorbid .68 .53 .54 .30 .28
• 2 - OTBM .81 .77 .77 .54 .53
• 3 - DTBM .81 .78 .80 .62 .62
• 4 - Attent/Work Mem .54 .68 .53 .37 .31
• 5 - ProcSpd/Ment Flex .64 .50 .55 .44 .44
• 6 - Verbal Reasoning .64 .54 .55 .36 .30
• 7 - Visual Reasoning .51 .70 .41 .45
• 8 - Verbal Memory .51 .62 .34 .32
• 9 - Visual Memory .70 .62 .37 .40
• 10 - Dom Motor/Sen .41 .34 .37 .53
• 11 - Nond Motor/Sen .45 .32 .40 .53
• All were Significant p lt .001

131
Domains Regression Equations

• Attention Working Memory
• (Verbal Reasoning) .315
• (Verbal Memory) .273
• (Processing Speed) .193
• Constant 10.972

132
Domains Regression Equations

• Processing Speed/ Mental Flexibility
• Verbal Reasoning .401
• Visual Reasoning .284
• Attention Working Memory .230
• Constant 2.434

133
Domains Regression Equations

• Verbal Reasoning
• Processing Speed .361
• Attention Working Memory .354
• Visual Reasoning .243
• Constant 2.5

134
Domains Regression Equations

• Visual Reasoning
• Visual Memory .322
• Processing Speed/Mental Flexibility .213
• Verbal Reasoning .208
• Constant 11.813

135
Domains Regression Equations

• Verbal Memory
• Attention Working Memory .738
• Visual Memory .388
• Constant -7.615

136
Domains Regression Equations

• Visual Memory
• Visual Reasoning .698
• Verbal Memory .311
• Processing Speed .0909
• Constant -5.517

137
Regression

• Adjusted SE
• R R2 of the Estimate
• Attent/Working Memory .79 .63 4.88
• Processing Speed .77 .60 5.31
• Verbal Reasoning .80 .64 5.04
• Visual Reasoning .78 .61 4.88
• Verbal Memory .75 .56 7.96
• Visual Memory .77 .59 7.11

138
Review

• Took a battery of well known tests
• Developed Norms
• Identified Validity, Reliability, Sensitivity and
  Specificity.
• Internal Validity Checks and Internal Consistency